Executive Summary
Overview
This study examines transport services and mobility challenges in
Kumasi Metropolitan Area, Ghana, based on surveys of 393
passengers and 339 transport operators across
four major routes.
Key Findings
Sample
Characteristics
Passenger
Demographics
Table 1: Passenger Demographic Characteristics (N=393)
|
Characteristic
|
Value
|
|
Sample Size
|
393
|
|
Female (%)
|
53.4%
|
|
Male (%)
|
46.6%
|
|
Mean Age Group
|
26–35 years (modal)
|
|
Tertiary Education (%)
|
23.7%
|
|
Self-employed/Trader (%)
|
30.3%
|
|
Income < GHC 1,000 (%)
|
19.6%
|
|
Long-term Residents (>7 years) (%)
|
69.2%
|
Detailed Demographic
Breakdown
Table 2: Detailed Demographic Distribution
|
Group
|
Category
|
Count
|
Percentage
|
|
Gender
|
|
Gender
|
Female
|
210
|
53.4%
|
|
Gender
|
Male
|
183
|
46.6%
|
|
Age Group
|
|
Age Group
|
18–25 years
|
106
|
27.0%
|
|
Age Group
|
26–35 years
|
100
|
25.4%
|
|
Age Group
|
36–45 years
|
87
|
22.1%
|
|
Age Group
|
46–55 years
|
59
|
15.0%
|
|
Age Group
|
56 years and above
|
27
|
6.9%
|
|
Age Group
|
Below 18 years
|
14
|
3.6%
|
|
Education
|
|
Education
|
Junior High School (JHS)
|
92
|
23.4%
|
|
Education
|
No formal education
|
21
|
5.3%
|
|
Education
|
Primary education
|
35
|
8.9%
|
|
Education
|
Senior High School (SHS)
|
152
|
38.7%
|
|
Education
|
Tertiary (College/University/Polytechnic)
|
93
|
23.7%
|
|
Income
|
|
Monthly Income (GHS)
|
Below GHC 500
|
37
|
10.1%
|
|
Monthly Income (GHS)
|
GHC 1,000 – 1,499
|
66
|
18.1%
|
|
Monthly Income (GHS)
|
GHC 1,500 – 1,999
|
88
|
24.1%
|
|
Monthly Income (GHS)
|
GHC 2,000 and above
|
134
|
36.7%
|
|
Monthly Income (GHS)
|
GHC 500 – 999
|
40
|
11.0%
|
Driver
Demographics
Table 3: Transport Operator Characteristics (N=339)
|
Characteristic
|
Value
|
|
Sample Size
|
339.0
|
|
All Male (%)
|
100.0
|
|
Age 26-45 years (%)
|
54.3
|
|
Basic Education (%)
|
36.3
|
|
Trotro Drivers (%)
|
54.3
|
|
>5 Years Experience (%)
|
49.6
|
|
Union Members (%)
|
66.4
|
|
Work Full Day (%)
|
69.9
|
OBJECTIVE 1: Range of
Transport Services
To identify the range of transport services provided in the Kumasi
Metropolitan Area
Transport Mode
Distribution
Table 4: Primary Transport Mode Usage (N=393)
|
Transport Mode
|
Users
|
Percentage
|
% of Sample
|
|
Trotro (Minibus)
|
367
|
93.4
|
93.4%
|
|
Taxi
|
114
|
29.0
|
29%
|
|
Tricycle (Pragya)
|
73
|
18.6
|
18.6%
|
|
Motorcycle (Okada)
|
19
|
4.8
|
4.8%
|
Visualization:
Transport Modes

Route-Specific
Analysis
Table 5: Transport Service Distribution by Route
|
study_route
|
Sample (n)
|
% Trotro
|
% Taxi
|
% Tricycle
|
% Motorcycle
|
Avg Fare (GHS)
|
Modal Diversity
|
|
Ejisu– Kejetia Road
|
123
|
90.2
|
17.9
|
7.3
|
0.8
|
6.6
|
2
|
|
Sofoline – Adum Road
|
107
|
98.1
|
55.1
|
15.0
|
5.6
|
15.6
|
3
|
|
Adum- Tafo Road
|
87
|
100.0
|
16.1
|
27.6
|
10.3
|
7.9
|
4
|
|
Kejetia– Santasi Road
|
76
|
84.2
|
25.0
|
31.6
|
3.9
|
8.8
|
3
|
Visualization:
Routes and Modes

Travel Purpose
Analysis
Table 6: Primary Travel Purposes
|
Purpose
|
Count
|
Percentage
|
% of Trips
|
|
Work/Business
|
295
|
75.1
|
75.1%
|
|
Shopping/Errands
|
155
|
39.4
|
39.4%
|
|
Social/Religious
|
111
|
28.2
|
28.2%
|
|
School/Education
|
63
|
16.0
|
16%
|
Transport
Frequency
Table 7: Weekly Public Transport Usage Frequency
|
Weekly Frequency
|
Count
|
Percentage
|
Cumulative %
|
|
1–2 times
|
63
|
16.0
|
16.0
|
|
3–5 times
|
140
|
35.6
|
51.7
|
|
6–7 times
|
81
|
20.6
|
72.3
|
|
More than 7 times
|
109
|
27.7
|
100.0
|
OBJECTIVE 2: Service
Quality Indicators
2.To determine the indicators for assessing the quality of transport
services
Service Quality Index
(SQI)
Table 8: Service Quality Index (SQI) Summary Statistics
|
Statistic
|
Value
|
|
Mean SQI
|
2.37
|
|
SD
|
0.45
|
|
Median
|
2.40
|
|
Min
|
1.00
|
|
Max
|
3.20
|
|
% Excellent (≥3.5)
|
0.00
|
|
% Good (2.5-3.49)
|
41.98
|
|
% Fair (1.5-2.49)
|
52.67
|
|
% Poor (<1.5)
|
5.34
|
Quality
Dimensions
Table 9: Service Quality Dimensions (Scale: 1=Poor, 4=Excellent)
|
Dimension
|
Mean Score
|
SD
|
Rating
|
|
Reliability
|
2.46
|
0.68
|
Fair
|
|
Affordability
|
2.46
|
0.65
|
Fair
|
|
Courtesy
|
2.43
|
0.78
|
Fair
|
|
Safety
|
2.37
|
0.71
|
Fair
|
|
Comfort
|
2.15
|
0.74
|
Fair
|
Visualization:
Quality Dimensions

Reliability Analysis
(Cronbach’s Alpha)
Table 10: Internal Consistency Reliability (Cronbach’s Alpha)
|
Statistic
|
Value
|
|
Cronbach’s Alpha
|
0.636
|
|
Raw Alpha
|
0.636
|
|
Standard Alpha
|
0.637
|
|
Average Correlation
|
0.260
|
|
Median Correlation
|
0.409
|
|
N Items
|
5.000
|
Notes: Cronbach’s Alpha >0.6 is considered acceptable for
exploratory research. Inter-item correlations indicate moderate internal
consistency among the 5 dimensions of the Service Quality Index.
Quality by
Demographics
Table 11: Service Quality Index by Gender and Age Group
|
gender
|
age_group
|
n
|
Mean SQI
|
SD
|
95% CI Lower
|
95% CI Upper
|
|
Male
|
Below 18 years
|
7
|
2.60
|
0.40
|
2.30
|
2.90
|
|
Female
|
Below 18 years
|
7
|
2.54
|
0.56
|
2.13
|
2.96
|
|
Male
|
18–25 years
|
59
|
2.54
|
0.46
|
2.43
|
2.66
|
|
Male
|
36–45 years
|
39
|
2.46
|
0.36
|
2.35
|
2.58
|
|
Female
|
26–35 years
|
53
|
2.46
|
0.35
|
2.36
|
2.55
|
|
Male
|
26–35 years
|
47
|
2.43
|
0.44
|
2.31
|
2.56
|
|
Female
|
18–25 years
|
47
|
2.38
|
0.39
|
2.27
|
2.49
|
|
Male
|
56 years and above
|
7
|
2.29
|
0.47
|
1.93
|
2.64
|
|
Female
|
36–45 years
|
48
|
2.27
|
0.48
|
2.13
|
2.40
|
|
Female
|
46–55 years
|
35
|
2.20
|
0.46
|
2.05
|
2.35
|
|
Male
|
46–55 years
|
24
|
2.19
|
0.46
|
2.01
|
2.38
|
|
Female
|
56 years and above
|
20
|
2.02
|
0.56
|
1.78
|
2.26
|
Statistical Test:
Gender Differences
Table 12: Independent Samples t-test - Service Quality by Gender
|
Group
|
Mean.SQI
|
t.statistic
|
p.value
|
Significance
|
|
Female
|
2.314
|
NA
|
NA
|
NA
|
|
Male
|
2.444
|
NA
|
NA
|
NA
|
|
Difference
|
-0.129
|
-2.855
|
0.005
|
**
|
Correlation
Matrix

OBJECTIVE 3: Transport
Service Challenges
3.To identify the challenges involved in using the existing transport
services
Problem
Frequency
Table 13: Most Common Transport Service Problems (N=393)
|
Problem
|
Count
|
Percentage
|
% Reporting
|
Rank
|
|
Overcrowding
|
253
|
64.4
|
64.4%
|
1
|
|
Long Waiting Times
|
234
|
59.5
|
59.5%
|
2
|
|
High Fares
|
192
|
48.9
|
48.9%
|
3
|
|
Reckless Driving
|
149
|
37.9
|
37.9%
|
4
|
|
Poor Road Conditions
|
97
|
24.7
|
24.7%
|
5
|
Visualization:
Problem Prevalence

Problems by Transport
Mode
Table 14: Transport Problems by Primary Mode
|
primary_mode
|
n
|
Long Waiting (%)
|
Overcrowding (%)
|
High Fares (%)
|
Reckless Driving (%)
|
Poor Roads (%)
|
|
Trotro
|
367
|
59.1
|
67.3
|
48.2
|
38.4
|
25.3
|
|
Taxi
|
18
|
77.8
|
16.7
|
50.0
|
27.8
|
16.7
|
|
Tricycle
|
6
|
33.3
|
33.3
|
83.3
|
50.0
|
16.7
|
|
Motorcycle
|
2
|
50.0
|
50.0
|
50.0
|
0.0
|
0.0
|
Chi-Square Tests
Table 15: Significant Problem-Mode Associations (Chi-Square Tests)
|
|
Problem
|
Mode
|
Chi.Square
|
df
|
p.value
|
Significant
|
|
X-squared1
|
Long Waiting
|
Taxi
|
23.980
|
1
|
0
|
***
|
|
X-squared2
|
Overcrowding
|
Trotro
|
18.824
|
1
|
0
|
***
|
Logistic Regression:
Overcrowding
Table 16: Logistic Regression - Predictors of Overcrowding Experience
|
Predictor
|
Odds Ratio
|
95% CI
|
p-value
|
Sig
|
|
age_group26–35 years
|
0.437
|
[0.21, 0.89]
|
0.024
|
|
|
monthly_incomeGHC 2,000 and above
|
0.363
|
[0.11, 1.08]
|
0.083
|
|
|
uses_trotro
|
9.049
|
[3.23, 28.7]
|
0.000
|
***
|
|
congestion_levelModerate
|
14.006
|
[2.35, 124.27]
|
0.007
|
**
|
|
congestion_levelSevere
|
9.845
|
[1.8, 81.99]
|
0.015
|
|
|
congestion_levelVery severe
|
11.658
|
[2.04, 100.15]
|
0.011
|
|
Model Performance: - Pseudo R² (McFadden) = 0.11 -
AIC = 459.1
Delay Frequency
Analysis
Table 17: Frequency of Transport Delays in Reaching Destination
|
Delay Frequency
|
Count
|
Percentage
|
Cumulative %
|
|
Rarely
|
27
|
6.9
|
6.9
|
|
Sometimes
|
163
|
41.5
|
48.3
|
|
Often
|
140
|
35.6
|
84.0
|
|
Always
|
63
|
16.0
|
100.0
|
OBJECTIVE 4: Mobility
Challenges
- To understand the mobility challenges among users of the current
transport services
Congestion
Analysis
Table 18: Traffic Congestion Severity Perception (N=393)
|
Congestion Level
|
Count
|
Percentage
|
|
Severe
|
192
|
48.9
|
|
Very severe
|
114
|
29.0
|
|
Moderate
|
76
|
19.3
|
|
Low
|
11
|
2.8
|
Visualization:
Congestion Levels

Daily Traffic
Time
Table 19: Time Spent in Traffic Daily (One-Way Commute)
|
Daily Traffic Time (One-Way)
|
Count
|
Percentage
|
|
Less than 30 minutes
|
98
|
24.9
|
|
30–60 minutes
|
168
|
42.7
|
|
1–2 hours
|
95
|
24.2
|
|
More than 2 hours
|
32
|
8.1
|
Peak Traffic
Times
Table 20: Most Common Peak Traffic Delay Periods
|
Peak Traffic Time
|
Count
|
Percentage
|
|
Evening [4–8 pm]
|
190
|
48.3
|
|
Morning [6–9 am]
|
132
|
33.6
|
|
Afternoon [12–3 pm]
|
70
|
17.8
|
|
Late night
|
1
|
0.3
|
Vulnerable
Groups
Table 21: Groups Perceived to Face Greatest Mobility Challenges
|
Group
|
Count
|
Percentage
|
Rank
|
|
Workers
|
269
|
68.4
|
1
|
|
Students
|
227
|
57.8
|
2
|
|
Traders/Market Women
|
188
|
47.8
|
3
|
|
Elderly
|
115
|
29.3
|
4
|
|
Persons with Disabilities
|
111
|
28.2
|
5
|
Visualization:
Vulnerable Groups

Multiple Linear
Regression: Travel Time
Table 22: Linear Regression - Predictors of Daily Travel Time
|
Predictor
|
Beta
|
Std. Error
|
95% CI
|
t-value
|
p-value
|
Sig
|
|
age_group56 years and above
|
0.343
|
0.179
|
[-0.009, 0.696]
|
1.913
|
0.056
|
|
|
occupationStudent
|
-0.277
|
0.130
|
[-0.532, -0.022]
|
-2.135
|
0.033
|
|
|
congestion_numeric
|
0.413
|
0.052
|
[0.31, 0.515]
|
7.940
|
0.000
|
***
|
|
study_routeEjisu– Kejetia Road
|
0.446
|
0.119
|
[0.212, 0.68]
|
3.751
|
0.000
|
***
|
|
study_routeKejetia– Santasi Road
|
-0.442
|
0.127
|
[-0.692, -0.191]
|
-3.468
|
0.001
|
***
|
|
uses_taxi
|
0.173
|
0.097
|
[-0.018, 0.365]
|
1.780
|
0.076
|
|
|
fare_per_trip
|
0.019
|
0.006
|
[0.007, 0.031]
|
3.201
|
0.001
|
**
|
Model Summary: - R² = 0.344 - Adjusted R² = 0.315 -
F-statistic = 11.59 (p < 0.001)
Ordinal Logistic
Regression: Service Quality
Table 23: Ordinal Logistic Regression - Predictors of Service Quality
Rating
|
Predictor
|
Odds Ratio
|
95% CI Lower
|
95% CI Upper
|
p-value
|
Sig
|
|
age_group26–35 years
|
1.916
|
0.993
|
3.695
|
0.052
|
|
|
age_group36–45 years
|
2.750
|
1.328
|
5.694
|
0.006
|
**
|
|
age_group46–55 years
|
2.035
|
0.915
|
4.525
|
0.081
|
|
|
education_levelTertiary (College/University/Polytechnic)
|
2.481
|
1.306
|
4.712
|
0.006
|
**
|
|
monthly_incomeGHC 1,000 – 1,499
|
0.246
|
0.099
|
0.613
|
0.003
|
**
|
|
monthly_incomeGHC 1,500 – 1,999
|
0.251
|
0.101
|
0.622
|
0.003
|
**
|
|
monthly_incomeGHC 2,000 and above
|
0.179
|
0.072
|
0.448
|
0.000
|
***
|
|
monthly_incomeGHC 500 – 999
|
0.429
|
0.162
|
1.137
|
0.089
|
|
|
congestion_levelVery severe
|
0.142
|
0.034
|
0.589
|
0.007
|
**
|
Interpretation: Odds Ratios > 1 indicate
increased likelihood of higher quality rating.
]
ADVANCED ANALYSIS:
Machine Learning
Data Preparation
## ML Dataset prepared: 365 complete cases
Random Forest: Mode
Switching
Table 24: Random Forest Performance - Mode Switching Prediction
|
|
Metric
|
Value
|
|
Accuracy
|
Accuracy
|
0.731
|
|
Sensitivity
|
Sensitivity
|
0.723
|
|
Specificity
|
Specificity
|
0.738
|
|
Precision
|
Precision
|
0.680
|
|
F1
|
F1-Score
|
0.701
|
Feature
Importance

Principal Component
Analysis

Table 26: Principal Component Loadings (First 3 Components)
|
Variable
|
PC1
|
PC2
|
PC3
|
|
afford_score
|
-0.416
|
-0.556
|
0.119
|
|
reliability_score
|
-0.474
|
-0.391
|
0.358
|
|
comfort_score
|
-0.422
|
0.502
|
0.523
|
|
safety_score
|
-0.455
|
0.529
|
-0.215
|
|
courtesy_score
|
-0.466
|
-0.078
|
-0.733
|
Variance Explained: - PC1: 40.9% - PC2: 22.2% - PC3:
15.9% - Cumulative: 79%
DRIVER ANALYSIS
Driver Demographics
& Operations
Table 27: Transport Operator Operational Characteristics
|
Metric
|
Value
|
|
Total Drivers
|
339.0
|
|
Avg Experience (years)
|
6.1
|
|
Union Members (%)
|
66.4
|
|
Full-time Operators (%)
|
69.9
|
|
High Daily Trips (7+) (%)
|
54.3
|
|
Poor Road Conditions (%)
|
42.5
|
|
Frequent Congestion (%)
|
73.7
|
|
Encounters Authority Challenges (%)
|
19.8
|
Congestion Causes
(Driver Perspective)
Table 28: Congestion Causes Identified by Drivers (N=339)
|
Cause
|
Count
|
Percentage
|
Rank
|
|
Driver Indiscipline
|
270
|
79.6
|
1
|
|
Poor Traffic Control
|
265
|
78.2
|
2
|
|
Street Trading/Hawking
|
176
|
51.9
|
3
|
|
Poor Road Network
|
123
|
36.3
|
4
|
|
Lack of Parking Space
|
117
|
34.5
|
5
|
Text Mining:
Authority Challenges
Table 29: Top 20 Words in Authority Challenge Descriptions
|
|
Word
|
Frequency
|
|
extortion
|
extortion
|
11
|
|
police
|
police
|
7
|
|
always
|
always
|
6
|
|
asking
|
asking
|
6
|
|
drivers
|
drivers
|
6
|
|
money
|
money
|
6
|
|
stops
|
stops
|
6
|
|
unwarranted
|
unwarranted
|
6
|
|
bribe
|
bribe
|
5
|
|
collection
|
collection
|
5
|
|
arguments
|
arguments
|
4
|
|
fines
|
fines
|
4
|
|
harassment
|
harassment
|
4
|
|
officers
|
officers
|
4
|
|
sometimes
|
sometimes
|
4
|
|
taking
|
taking
|
4
|
|
unapproved
|
unapproved
|
4
|
|
verbal
|
verbal
|
4
|
|
collecting
|
collecting
|
3
|
|
fees
|
fees
|
3
|
Improvement
Suggestions
Table 31: Driver Suggestions for Mobility Improvement
|
Suggestion
|
Count
|
Percentage
|
Rank
|
|
Better Traffic Management
|
299
|
88.2
|
1
|
|
Enforcement of Traffic Laws
|
291
|
85.8
|
2
|
|
More Parking/Loading Spaces
|
271
|
79.9
|
3
|
|
Construction of New Roads
|
115
|
33.9
|
4
|
|
Reduction of Street Hawking
|
101
|
29.8
|
5
|
COMPARATIVE
ANALYSIS
Driver vs Passenger
Perspectives
Table 32: Comparative Challenge Perspectives
|
Challenge
|
Driver_Pct_Driver
|
Driver_Pct_Passenger
|
Passenger_Pct_Driver
|
Passenger_Pct_Passenger
|
|
Poor Roads
|
36.3
|
0
|
0
|
24.7
|
|
Indiscipline
|
79.6
|
0
|
0
|
0.0
|
|
Street Trading
|
51.9
|
0
|
0
|
0.0
|
|
Poor Traffic Control
|
78.2
|
0
|
0
|
0.0
|
|
Long Waiting
|
0.0
|
0
|
0
|
59.5
|
|
Overcrowding
|
0.0
|
0
|
0
|
64.4
|
|
High Fares
|
0.0
|
0
|
0
|
48.9
|
Visualization:
Perspective Comparison

Route Comparison
Table 33: Comparative Route Analysis
|
study_route
|
Driver Sample
|
% Always Congested
|
% Poor Roads
|
Passenger Sample
|
Avg Fare (GHS)
|
% Severe Congestion
|
|
Adum - Tafo Road
|
124
|
50.8
|
62.9
|
87
|
7.9
|
89.7
|
|
Ejisu - Kejetia Road
|
117
|
16.2
|
31.6
|
123
|
6.6
|
76.4
|
|
Kejetia - Santasi Road
|
96
|
55.2
|
28.1
|
76
|
8.8
|
61.8
|
|
Sofoline - Adum Road
|
2
|
0.0
|
100.0
|
107
|
15.6
|
81.3
|
POLICY
RECOMMENDATIONS
Infrastructure
Preferences
Table 34: Infrastructure Improvement Priorities
|
Improvement
|
Support_Count
|
Support (%)
|
Priority
|
|
Expand Road Networks
|
269
|
68.4
|
High Priority
|
|
Improve Traffic Management
|
230
|
58.5
|
Medium Priority
|
|
Develop Bus Rapid Transit (BRT)
|
188
|
47.8
|
Medium Priority
|
|
Build Pedestrian Walkways
|
140
|
35.6
|
Lower Priority
|
Modern Transport
Systems Support
Table 35: Public Support for Modern Transport Systems (Light Rail, BRT)
|
Response
|
n
|
Percentage
|
|
Do Not Support
|
57
|
14.5
|
|
Support
|
336
|
85.5
|
CONCLUSIONS
Key Findings
Summary
Research Objective
1: Range of Transport Services
Research Objective
2: Service Quality Indicators
Research Objective
3: Service Challenges
Research Objective
4: Mobility Challenges
Machine Learning
Insights
- Random Forest Accuracy: for mode switching
prediction